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感染模型和 PK/PD 模型在优化重症感染危重症患者治疗中的作用。

The role of infection models and PK/PD modelling for optimising care of critically ill patients with severe infections.

机构信息

Department of Medical Sciences, Section of Infectious Diseases, Uppsala University, Uppsala, Sweden.

Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK.

出版信息

Intensive Care Med. 2017 Jul;43(7):1021-1032. doi: 10.1007/s00134-017-4780-6. Epub 2017 Apr 13.

DOI:10.1007/s00134-017-4780-6
PMID:28409203
Abstract

Critically ill patients with severe infections are at high risk of suboptimal antimicrobial dosing. The pharmacokinetics (PK) and pharmacodynamics (PD) of antimicrobials in these patients differ significantly from the patient groups from whose data the conventional dosing regimens were developed. Use of such regimens often results in inadequate antimicrobial concentrations at the site of infection and is associated with poor patient outcomes. In this article, we describe the potential of in vitro and in vivo infection models, clinical pharmacokinetic data and pharmacokinetic/pharmacodynamic models to guide the design of more effective antimicrobial dosing regimens. Individualised dosing, based on population PK models and patient factors (e.g. renal function and weight) known to influence antimicrobial PK, increases the probability of achieving therapeutic drug exposures while at the same time avoiding toxic concentrations. When therapeutic drug monitoring (TDM) is applied, early dose adaptation to the needs of the individual patient is possible. TDM is likely to be of particular importance for infected critically ill patients, where profound PK changes are present and prompt appropriate antibiotic therapy is crucial. In the light of the continued high mortality rates in critically ill patients with severe infections, a paradigm shift to refined dosing strategies for antimicrobials is warranted to enhance the probability of achieving drug concentrations that increase the likelihood of clinical success.

摘要

重症感染的危重症患者存在抗菌药物剂量不足的高风险。这些患者的抗菌药物药代动力学(PK)和药效动力学(PD)与从其数据中开发常规剂量方案的患者群体有很大不同。使用这些方案通常会导致感染部位的抗菌药物浓度不足,与患者预后不良相关。在本文中,我们描述了体外和体内感染模型、临床药代动力学数据和药代动力学/药效动力学模型在指导更有效的抗菌药物剂量方案设计方面的潜力。基于影响抗菌药物 PK 的人群 PK 模型和患者因素(如肾功能和体重)的个体化剂量可提高达到治疗药物暴露的概率,同时避免毒性浓度。当应用治疗药物监测(TDM)时,可以根据个体患者的需求进行早期剂量调整。TDM 可能对感染性危重症患者尤其重要,因为这些患者存在深刻的 PK 变化,迅速给予适当的抗生素治疗至关重要。鉴于重症感染的危重症患者的死亡率仍然很高,需要对抗菌药物的剂量方案进行精细调整,以提高增加临床成功可能性的药物浓度的概率。

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